With the rapid development of sensor and communication technologies, a large amount of spatiotemporal traffic data has been accumulated, presenting the characteristics of big data. The potential information and regularity of traffic state evolution can be extracted from the huge traffic flow time series data and applied to intelligent transportation systems. This study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network. Transfer entropy algorithm is utilized to detect the spatiotemporal causality of network traffic states based on the extensive traffic time series data, which could measure the amount and direction of information transmission. A combination of Gaussian kernel density estimation and sliding window approach is proposed to calculate the transfer entropy and construct dynamic spatiotemporal causality graphs based on the causality significance test. The indexes of affected coefficient, influence coefficient, input degree, and output degree are defined to evaluate the causal interaction of traffic states among different road segments and identify the critical roads and potential bottlenecks of the existing road network. Experimental results based on real-world traffic sensor data indicate that the structures of traffic causality graphs are time-varying; the traffic cause-effect interaction among different road segments during the peak time is more significant than that during the nonpeak time; and the critical road segments can be identified, which are mainly located at the intersections of arterial roads, undertaking the convergence and dispersion of large traffic flows.

The sharing mode of the logistics industry can effectively solve the new problems arising from the rapid development of the express industry. However, only when the interests are reasonably distributed can the sharing mode be implemented for a long time. This paper discusses the connotation of unified warehouse and distribution, designs the operation mode of a unified warehouse and distribution, and solves the profit distribution problem of a unified warehouse and distribution alliance based on the improved Shapley value method. Firstly, the traditional Shapley value method is improved by using a comprehensive correction factor, including the proportions of investment, risk, and innovative research contributions. Secondly, each factor’s weight is determined by the analytic hierarchy process (AHP), and the profits are distributed according to the contribution of each express enterprise to the alliance. Finally, an example is given to verify the validity of the modified algorithm. It proves that the modified Shapley value method can effectively solve the problem of profit distribution.

Nowadays, intelligent mobile devices become most wide mobile multimedia terminals. But limited by performance and battery life, traditional audio architectures and algorithms cannot meet increasingly complex processing requirements. In this paper, the traditional audio convolution algorithm and audio architecture are analyzed. Based on the optimization of the block convolution algorithm, and combined with the cloud GPU, a dynamic task allocation low latency convolution algorithm is proposed under cloud hybrid systems. The experimental results show that the convolution algorithm can effectively reduce the workload of the mobile terminal, reduce the CPU usage of the mobile terminal, and realize the real-time audio mixing processing in complex scenes on mobile terminals with limited performance.